Pacific
Symposium on Biocomputing
2025
Session Name:
AI and Machine Learning in Clinical Medicine: Generative and Interactive
Systems at the Human-Machine Interface
Short
Description:
In a wave of disruptive technology, large language model chatbots
are giving access to interactive systems capable of surpassing humans in
clinical reasoning, while generative image models blur the distinction between
fabricated vs. real information and intelligence. This session will showcase
cutting edge research invoking such methods to enhance patient care through
clinical decision support, monitoring tools, image interpretation, and triaging
capabilities, even as in-depth studies are needed to assess the impact and
implications of such systems on human lives.
Solicitation
Webpage Content:
Machine learning technologies have transformed the capacity to
analyze multi-dimensional and complex medical datasets. The advent of
generative AI has further given rise to sophisticated large language models
(LLMs) and text-to-image generators with dynamic interactive capabilities.
Utilizing these advancements can improve patient care by strengthening clinical
decision-making, enhancing monitoring, interpreting medical images, optimizing
triage processes, and more.
In this session, we invite submissions
within the broad spectrum of emerging machine learning advancements that offer
solutions to solve healthcare challenges. Our focus is on research areas that
demonstrate how AI can address specific clinical needs. While we anticipate
some algorithms may need further refinement for clinical application, we
encourage submissions that propose clear, actionable use cases within the
healthcare domain. We are particularly interested in papers that cover a
variety of research topics, such as predictive analytics for patient outcomes,
AI-driven personalized medicine approaches, natural language processing,
federated learning, and LLMs for improved patient interaction and documentation
which showcase the power of collaborative AI model development while upholding
data privacy and compliance and enhancing diagnostic accuracy. Our session will
be dedicated exclusively to the clinical applications of these methodologies
and excludes multi-omics methods that are well covered by other PSB sessions.
Our goal is to promote discussions that explore how researchers in machine
learning can collaborate with healthcare practitioners to enhance the
efficiency and effectiveness of modern healthcare systems.
Session
topics
The session is interested in
research on the applications of emerging artificial intelligence models in
solving real-wor
Below are examples of submission
topics that would be of interest:
●
Generative
artificial intelligence methods to solve real-world problems in healthcare.
●
Rigorous evaluation
of large language models and chatbots in analyzing clinical notes and solving
narrow and well-defined healthcare tasks.
●
Generative image and
video processing models for medical image and video analysis.
●
Multi-modal
healthcare data analysis using artificial intelligence models to solve
well-defined clinical tasks.
●
Clinical validation
of language and image analysis models.
●
Novel applications
of artificial intelligence in healthcare.
●
Computational
methods for public health that can screen large populations with high
specificity.
●
Computational
approaches to analyze data, especially of varied types, to help inform
diagnosis, including decision support tools to help streamline diagnosis or
treatment.
●
Methods for
integrating the most up to date literature evidence and guidelines into
clinical practice.
●
Tools for analyzing
multi-modal data such as lifestyle, environmental, geographic, and healthcare
records to gain new insights for delivering better or tailored clinical care.
●
Tools or methods
that aid in data-centric artificial intelligence, with applications to medical
tasks.
●
Tools and methods
for assessing bias in artificial intelligence algorithms.
●
Tools and methods
that aid in machine learning auditing and monitoring in the healthcare system.
●
Tools or methods
that aid in interpretability or explainability for machine learning in
healthcare.
Session
Organizers
|
|
Dr. Jonathan Chen, MD/PhD is an Assistant Professor of Medicine
and works with the Stanford Center for Biomedical Informatics Research at
Stanford School of Medicine. He is a practicing physician who holds a PhD in
computer science and has worked on clinical decision support tools using
machine learning. |
|
|
Dr. Roxana Daneshjou, MD/PhD is a
board-certified dermatologist and Assistant Professor in Biomedical Data
Science and Dermatology at Stanford. |
|
|
Dr. Dokyoon Kim is an associate
professor of informatics in Biostatistics and Epidemiology at the University
of Pennsylvania. As a Senior Fellow at the Institute of Biomedical
Informatics and Associate Director of Informatics for Immune Health at the
Perelman School of Medicine, Dr. Kim brings robust expertise in the
integration of AI into translational informatics. He also serves as the
Director of the Center for AI-Driven Translational Informatics (CATI). |
|
|
Dr. Joseph D. Romano, PhD, MPhil, MA is an Assistant Professor
of Informatics and Pharmacology at the University of Pennsylvania. He is an
expert in the integration and analysis of clinical and environmental health
data using graph machine learning and other AI-based techniques,
and is a founding member of the NIH/NIEHS Environmental Health
Language Collaborative. |
|
|
Fateme Nateghi
Haredasht, PhD is a postdoctoral scholar at the
Stanford Center for Biomedical Informatics Research where she is advancing
machine learning integration in healthcare to unravel complex healthcare
challenges and improve patient outcomes. |
|
|
Dr. Geoff Tison, MD MPH is a
practicing cardiologist, Associate Professor and Co-Director of the Center
for Biosignal Research at the University of
California, San Francisco. He leads a computational research lab at
UCSF (tison.ucsf.edu) that aims to
improve cardiovascular disease prevention by applying artificial intelligence
and statistical methods to large-scale medical data. |
Submission
Information
Important dates
●
August 1, 2024: Call for papers deadline (no extensions will be granted)
●
September 9, 2024: Notification of paper acceptance.
●
October 1, 2024: Camera-ready final papers deadline.
●
December 2, 2024: Poster abstract submission deadline.
●
January 4-8, 2025: Conference dates
●
All deadlines are due by 11:59pm PT
Paper Format and Submission Portal
Please see the PSB paper format
template and instructions at http://psb.stanford.edu/psb-online/psb-submit.
Paper Submissions
Unlike the abstracts at most
biology conferences, papers in the PSB proceedings are archival, rigorously
peer-reviewed publications. PSB publications are Open Access and linked
directly from MEDLINE/PubMed and Google Scholar for wide accessibility. They
should be thought of as short journal articles that may be cited on CVs and
grant reports.
Travel Fellowships for Trainees
PSB traditionally provides
fellowships for select trainees. The application process opens upon paper
acceptance. Individuals from underrepresented communities are particularly
encouraged to participate in the conference and apply for travel support.
Poster Format and Submission Portal
Poster presenters will be
provided with an easel and a poster board 32" x 40" (80x100cm) either
portrait or landscape orientation is acceptable. One poster from each paid
participant is permitted. See the submission portal web site for the instructions
regarding poster submissions.